1. Field of the Invention
The present invention relates to a method and apparatus for recognizing a face by using a neural network, and more particularly, to a face recognition method and apparatus in which after a neural network is trained by using a predetermined face image, the characteristics of a face image that is the object of recognition are extracted and provided to the trained neural network, and by determining whether or not the object face image is the same as the trained face image, a face is recognized.
2. Description of the Related Art
In the process of moving toward a 21st century information society, information of organizations as well as individuals has become increasingly important. In order to protect this important information, a variety of passwords are used and other technologies capable of confirming identity are strongly demanded. Among the technologies, a face recognition technology is regarded as one of the most convenient and competitive identification methods, because it has advantages in which identification is performed without any specific motion or action of a user, and even when the user does not perceive it.
Beyond protection of information, social demands for confirmation of identity as in a credit card, a cash card, and an electronic resident registration certificate, are continuously increasing. However, since there has been no supplementary identification method except a password till now, a lot of social problems such as computer crimes have been committed. The face recognition technology is highlighted as a method to solve these social problems. In addition, the face recognition technology has a variety of application fields, such as terminal access control, public place access control systems, electronic albums, and recognition of photos of criminals, and is appreciated as a very useful technology in the information society.
Among the face recognition technologies that are widely used at present, there is a technology in which a face is recognized by applying principal component analysis (PCA) to a face image. The PCA is a technique by which image data is projected to a low-dimension eigenvector space with minimizing loss of eigen-information of an image itself so that information is reduced. Among face recognition methods using the PCA, a method using a pattern classifier, which is trained with principal component vectors extracted from an image which is registered in advance, was widely used. However, the recognition speed and reliability of this method are low if large number of data is used for face recognition. In addition, this method cannot provide a satisfying result for changes in a facial expression or in the pose of a face though it provides strong light resistance as it uses a PCA base vector.
Meanwhile, the performance of face recognition is determined by the performance of a pattern classifier which distinguishes a registered face from an unregistered face. As a method for training this pattern classifier, there is a method using an artificial neural network (hereinafter referred to as “ANN”).
FIG. 1 is a block diagram of an example of a face recognition apparatus using an ANN-PCA method.
When a 2-dimensional window having a size defined based on experiments is defined as a paxel, the face recognition apparatus of FIG. 1 performs eigenfiltering (or convolution) with moving an N×N size paxel window. At this time, a paxel window indicates M eigenpaxel kernels obtained by applying the PCA method to each block after an arbitrary learning image is divided into blocks of a predetermined size. Since the eigenfiltered image has too high dimensions as an input vector to a neural network, the dimension of the vector is reduced through a sub-sampling technique so that the image can be input to the neural network.
However, without considering the characteristic of an eigenpaxel kernel, the face recognition apparatus of FIG. 1 uses all input vectors as input values for the neural network. Accordingly, the amount of computation for neural network learning increases, which in turn lengthens a time taken for the computation, and degrades recognition performance and convergence.